Library & Dataset

Using OLR

Inspect Dataset Using Training and Validation

OLR Equations

Using all variables

Inspect Dataset Using Training and Validation

Training set divide in the Diagnostic_based.on.Harell.Rmd file

vclust <- varclus (~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage , data=train.data)

# took out density since training has 0 d4 and it was not allowing do the plot

p <- plot(vclust)

par(mfrow=c(6,5))
plot.xmean.ordinaly (risk~angle+brick+wood+mixed+ density+EN +TC + TC_mature_soil + TC_saprolite_soil +  TC_weath_rock  + TC_unstable_structure  + T_construction  + spring +  landfill + garbage  + crack + leaning_wall + scars + downward_floor + tilted + fracture + conc_rainfall + wastewater + leak + septic_tank  + tree + ground_veg + deforestation + banana + drainage, data=train.data, cr=TRUE , subn=FALSE)

#angle + building+density+EN +TC + TC_mature_Soil + TC_saprolito +  TC_weath_rock + TC_rock + TC_geol_desfav + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + DepTaludeAterro + aterro + lixo + entulho + crack + belly_wall + scars + drawback + tilted + fracture + conc_rainfall_water + wastewater + leak + septic_tank + drainage + tree + ground_veg + deforestation + banana

Diagnostic 2: Proportion (-5% of one of the parameters based on what is expected. Since some parameters have 2 predictors, others 5)

#library(plyr)
brick <- count(train.data$brick) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "brick")

wood <- count(train.data$wood) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wood")

mixed <- count(train.data$mixed) %>% 
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "mixed")

TC_mature_soil <- count(train.data$TC_mature_soil) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_mature_soil")

T_construction  <- count(train.data$T_construction ) %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "T_construction ")

spring <- count(train.data$spring) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "spring")

landfill <- count(train.data$landfill) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "landfill")

garbage <- count(train.data$garbage) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "garbage")

crack <- count(train.data$crack) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "crack")

leaning_wall <- count(train.data$leaning_wall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "leaning_wall")

scars <- count(train.data$scars) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "DepTaludeAterro")

downward_floor <- count(train.data$downward_floor) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "scars")

tilted <- count(train.data$tilted) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tilted")

conc_rainfall <- count(train.data$conc_rainfall) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall")

wastewater <- count(train.data$wastewater) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "wastewater")

leak <- count(train.data$leak) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "conc_rainfall_water")

septic_tank <- count(train.data$septic_tank) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "septic_tank")

angle <- count(train.data$angle) # angle A less than 5% but the rest are okay (3,50, 91, 277, 109) Expected=106
angle <- angle %>%
  mutate("Percentage"=(freq/106)*100)%>%
  mutate("Classifier" = "angle")

EN <- count(train.data$EN) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "EN")

TC <- count(train.data$TC)  %>%
  mutate ("Percentage"=(freq/265)*100) %>%
  mutate("Classifier" = "TC")

TC_saprolite_soil  <- count(train.data$TC_saprolite_soil )  %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_saprolite_soil ")

banana <- count(train.data$banana) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "banana")

drainage <- count(train.data$drainage) %>%
  mutate ("Percentage"=(freq/176.7)*100)%>%
  mutate("Classifier" = "drainage")

deforestation <- count(train.data$deforestation) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "deforestation")

TC_unstable_structure  <- count(train.data$TC_unstable_structure ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_unstable_structure ")


tree <- count(train.data$tree) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "tree")

ground_veg <- count(train.data$ground_veg) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "ground_veg")


density <- count(train.data$density)  %>% #(79, 415, 36) # d4 =0 
  mutate ("Percentage"=(freq/132.5)*100)%>%
  mutate("Classifier" = "density")

TC_weath_rock  <- count(train.data$TC_weath_rock ) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "TC_weath_rock ")

fracture <- count(train.data$fracture) %>%
  mutate ("Percentage"=(freq/265)*100)%>%
  mutate("Classifier" = "fracture")









df <- rbind(brick, wood, mixed, TC_mature_soil, T_construction, spring, landfill, garbage, crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall, wastewater, leak, septic_tank, angle, EN, TC, TC_saprolite_soil,  banana, drainage, deforestation, TC_unstable_structure, tree, ground_veg,density, TC_weath_rock, fracture)

df

##        x freq  Percentage             Classifier
## 1  FALSE   36  13.5849057                  brick
## 2   TRUE  494 186.4150943                  brick
## 3  FALSE  456 172.0754717                   wood
## 4   TRUE   74  27.9245283                   wood
## 5  FALSE  493 186.0377358                  mixed
## 6   TRUE   37  13.9622642                  mixed
## 7  FALSE  260  98.1132075         TC_mature_soil
## 8   TRUE  270 101.8867925         TC_mature_soil
## 9  FALSE  224  84.5283019        T_construction 
## 10  TRUE  306 115.4716981        T_construction 
## 11 FALSE  512 193.2075472                 spring
## 12  TRUE   18   6.7924528                 spring
## 13 FALSE  336 126.7924528               landfill
## 14  TRUE  194  73.2075472               landfill
## 15 FALSE  357 134.7169811                garbage
## 16  TRUE  173  65.2830189                garbage
## 17 FALSE  445 167.9245283                  crack
## 18  TRUE   85  32.0754717                  crack
## 19 FALSE  498 187.9245283           leaning_wall
## 20  TRUE   32  12.0754717           leaning_wall
## 21 FALSE  320 120.7547170        DepTaludeAterro
## 22  TRUE  210  79.2452830        DepTaludeAterro
## 23 FALSE  474 178.8679245                  scars
## 24  TRUE   56  21.1320755                  scars
## 25 FALSE  435 164.1509434                 tilted
## 26  TRUE   95  35.8490566                 tilted
## 27 FALSE   18   6.7924528          conc_rainfall
## 28  TRUE  512 193.2075472          conc_rainfall
## 29 FALSE  206  77.7358491             wastewater
## 30  TRUE  324 122.2641509             wastewater
## 31 FALSE  346 130.5660377    conc_rainfall_water
## 32  TRUE  184  69.4339623    conc_rainfall_water
## 33 FALSE  527 198.8679245            septic_tank
## 34  TRUE    3   1.1320755            septic_tank
## 35     C   28  26.4150943                  angle
## 36     D  125 117.9245283                  angle
## 37     E  377 355.6603774                  angle
## 38 FALSE  349 131.6981132                     EN
## 39  TRUE  181  68.3018868                     EN
## 40 FALSE   26   9.8113208                     TC
## 41  TRUE  504 190.1886792                     TC
## 42 FALSE  445 167.9245283     TC_saprolite_soil 
## 43  TRUE   85  32.0754717     TC_saprolite_soil 
## 44 FALSE  361 136.2264151                 banana
## 45  TRUE  169  63.7735849                 banana
## 46     Y   70  39.6151669               drainage
## 47     P  241 136.3893605               drainage
## 48     N  219 123.9388795               drainage
## 49 FALSE  489 184.5283019          deforestation
## 50  TRUE   41  15.4716981          deforestation
## 51 FALSE  517 195.0943396 TC_unstable_structure 
## 52  TRUE   13   4.9056604 TC_unstable_structure 
## 53 FALSE  203  76.6037736                   tree
## 54  TRUE  327 123.3962264                   tree
## 55 FALSE  160  60.3773585             ground_veg
## 56  TRUE  370 139.6226415             ground_veg
## 57    d1   72  54.3396226                density
## 58    d2  421 317.7358491                density
## 59    d3   37  27.9245283                density
## 60 FALSE  518 195.4716981         TC_weath_rock 
## 61  TRUE   12   4.5283019         TC_weath_rock 
## 62 FALSE  529 199.6226415               fracture
## 63  TRUE    1   0.3773585               fracture

TC_weath_rock, TC_rock_TC_geol_desf, fracture, TC_rock

OF: angle (slope angle), building (building material), EN (natural slope), TC_saprolito (cut slope with saprolite), Taterro (slope with landfill), DepEncostaNatural (natural slope with construction material), DepTaludeAterro (slope with landfill and construction material), DepTaludeCorte (cut slope with construction material), landfill, garbage, construction_deposit (construction material), crack, leaning_wall, scars, downward_floor, tilted, conc_rainfall_water (concentration of rainfall water), wastewater, leak, drainage
MOF:TC_mature_soil (slope cut with soil), density, TC (slope cut), - - TC_geol_desfav (cut slope with geology favorable to instability), tree, ground_veg (ground vegetation), deforestation, banana
NOF: TC_weath_rock (cut slope with weathering rock), TC_rock (cut slope in bedrock), fracture, septic_tank
GP:all classifiers minus BP classifiers
BP: TC_weath_rock (True N= 11 , False N= 529), TC_geol_desf (True N= 11 , False N= 529), fracture (True N=1 , False N=529), TC_rock (True N=1 , False N=529 ), septic_tank (True N=4 , False N=526 )

Equation 1

consider all parameter okay from first diagnostic:

f1 <- lrm(risk ~ building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + tree + ground_veg + banana , data=train.data, x=TRUE , y=TRUE)

# Equation 1

eq_OLR_01 <- polr(risk ~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil, data= train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_01))



p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                             Value Std. Error     t value      p value
## brickTRUE             -1.06800359  0.4434310 -2.40850014 8.009110e-03
## woodTRUE               1.26107034  0.3400823  3.70813294 1.043965e-04
## ENTRUE                 0.57035097  0.3740951  1.52461471 6.367761e-02
## TC_mature_soilTRUE     0.54250106  0.2184423  2.48349789 6.504955e-03
## T_constructionTRUE     0.70778568  0.3648985  1.93967829 2.620940e-02
## springTRUE            -0.96403829  0.6418554 -1.50195567 6.655428e-02
## landfillTRUE          -0.17918500  0.3313524 -0.54076869 2.943335e-01
## leakTRUE              -0.17063556  0.2360242 -0.72295782 2.348529e-01
## garbageTRUE           -0.22130183  0.2977924 -0.74314129 2.286981e-01
## crackTRUE              1.97407258  0.3344921  5.90170203 1.798853e-09
## leaning_wallTRUE       2.00490794  0.5206348  3.85089128 5.884437e-05
## scarsTRUE              3.80117201  0.3435832 11.06332270 9.448250e-29
## downward_floorTRUE     1.28453811  0.3836629  3.34809052 4.068521e-04
## tiltedTRUE             1.05358907  0.3177892  3.31537042 4.576088e-04
## septic_tankTRUE        0.04304422  1.4490197  0.02970575 4.881509e-01
## conc_rainfallTRUE      1.87732511  0.5431539  3.45634125 2.737808e-04
## wastewaterTRUE         0.60520085  0.2331554  2.59569686 4.719965e-03
## ground_vegTRUE         0.71098812  0.2473930  2.87392118 2.027051e-03
## angleD                 0.35595417  0.4883695  0.72886233 2.330429e-01
## angleE                 0.18581360  0.5641278  0.32938214 3.709334e-01
## TC_saprolite_soilTRUE  0.04327260  0.2928845  0.14774632 4.412715e-01
## R1|R2                  0.31194934  0.8956919  0.34827750 3.638159e-01
## R2|R3                  4.54083034  0.9362909  4.84980732 6.179073e-07
## R3|R4                  9.59071214  1.0254985  9.35224400 4.290377e-21

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ======================================================
##                       Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE             -1.07    0.44     -2.41   0.01  
## woodTRUE              1.26     0.34     3.71   0.0001 
## ENTRUE                0.57     0.37     1.52    0.06  
## TC_mature_soilTRUE    0.54     0.22     2.48    0.01  
## T_constructionTRUE    0.71     0.36     1.94    0.03  
## springTRUE            -0.96    0.64     -1.50   0.07  
## landfillTRUE          -0.18    0.33     -0.54   0.29  
## leakTRUE              -0.17    0.24     -0.72   0.23  
## garbageTRUE           -0.22    0.30     -0.74   0.23  
## crackTRUE             1.97     0.33     5.90      0   
## leaning_wallTRUE      2.00     0.52     3.85   0.0001 
## scarsTRUE             3.80     0.34     11.06     0   
## downward_floorTRUE    1.28     0.38     3.35   0.0004 
## tiltedTRUE            1.05     0.32     3.32   0.0005 
## septic_tankTRUE       0.04     1.45     0.03    0.49  
## conc_rainfallTRUE     1.88     0.54     3.46   0.0003 
## wastewaterTRUE        0.61     0.23     2.60    0.005 
## ground_vegTRUE        0.71     0.25     2.87    0.002 
## angleD                0.36     0.49     0.73    0.23  
## angleE                0.19     0.56     0.33    0.37  
## TC_saprolite_soilTRUE 0.04     0.29     0.15    0.44  
## R1| R2                0.31     0.90     0.35    0.36  
## R2| R3                4.54     0.94     4.85   0.0000 
## R3| R4                9.59     1.03     9.35      0   
## ------------------------------------------------------

less p-value = 0.10 (TC_saprolitoTRUE,TaterroTRUE, DepTaludeAterroTRUE,DepTaludeAterroTRUE,landfillTRUE, construction_depositTRUE, leakTRUE)

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=2, cex.sub=2, cex.main=2)

Creating function with four level

Equation 1

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN +  TC_mature_soil + T_construction + spring+ landfill+ leak+ garbage+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ septic_tank+ conc_rainfall+ wastewater+ ground_veg + angle + TC_saprolite_soil
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+----------+------------+----------+
## |                 |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +-----------------+---+---+----+----------+------------+----------+
## |brick            |No | 36|Inf | 2.8332133| 1.609437912|-0.4519851|
## |                 |Yes|493|Inf | 2.2981307|-0.101506946|-2.0955154|
## +-----------------+---+---+----+----------+------------+----------+
## |wood             |No |455|Inf | 2.2094947|-0.198450939|-2.2598232|
## |                 |Yes| 74|Inf | 3.5835189| 1.369487243|-0.6729445|
## +-----------------+---+---+----+----------+------------+----------+
## |EN               |No |348|Inf | 1.9070703|-0.443931389|-2.4756043|
## |                 |Yes|181|Inf | 4.4942386| 0.881738350|-1.2280704|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_mature_soil   |No |260|Inf | 1.8941745|-0.185142433|-2.2407097|
## |                 |Yes|269|Inf | 2.9802281| 0.171422266|-1.6593349|
## +-----------------+---+---+----+----------+------------+----------+
## |T_construction   |No |224|Inf | 1.5567942|-0.894516932|-3.1734135|
## |                 |Yes|305|Inf | 3.6142906| 0.629865731|-1.4277941|
## +-----------------+---+---+----+----------+------------+----------+
## |spring           |No |511|Inf | 2.2897370|-0.035228692|-2.0171228|
## |                 |Yes| 18|Inf |       Inf| 0.955511445|-0.2231436|
## +-----------------+---+---+----+----------+------------+----------+
## |landfill         |No |335|Inf | 1.8632184|-0.493350993|-2.6075090|
## |                 |Yes|194|Inf | 4.5643482| 0.876929658|-1.1972838|
## +-----------------+---+---+----+----------+------------+----------+
## |leak             |No |345|Inf | 1.9760632|-0.297834444|-2.4266973|
## |                 |Yes|184|Inf | 3.5779479| 0.557481315|-1.2809338|
## +-----------------+---+---+----+----------+------------+----------+
## |garbage          |No |357|Inf | 2.0421701|-0.299129458|-2.3529102|
## |                 |Yes|172|Inf | 3.3202283| 0.624154309|-1.2943569|
## +-----------------+---+---+----+----------+------------+----------+
## |crack            |No |444|Inf | 2.1822989|-0.317967468|-2.6246686|
## |                 |Yes| 85|Inf | 3.7256934| 2.264363880|-0.2125614|
## +-----------------+---+---+----+----------+------------+----------+
## |leaning_wall     |No |497|Inf | 2.2591000|-0.132992454|-2.1905356|
## |                 |Yes| 32|Inf |       Inf|         Inf| 0.2513144|
## +-----------------+---+---+----+----------+------------+----------+
## |scars            |No |319|Inf | 1.7808304|-1.421941700|-4.1399551|
## |                 |Yes|210|Inf | 5.3423343| 3.228826156|-0.8472979|
## +-----------------+---+---+----+----------+------------+----------+
## |downward_floor   |No |473|Inf | 2.2042917|-0.225046501|-2.2524607|
## |                 |Yes| 56|Inf |       Inf| 3.295836866|-0.3610133|
## +-----------------+---+---+----+----------+------------+----------+
## |tilted           |No |434|Inf | 2.1082771|-0.392042088|-2.4336134|
## |                 |Yes| 95|Inf |       Inf| 2.696876901|-0.6306268|
## +-----------------+---+---+----+----------+------------+----------+
## |septic_tank      |No |526|Inf | 2.3215530|-0.007604599|-1.9073615|
## |                 |Yes|  3|Inf |       Inf| 0.693147181|      -Inf|
## +-----------------+---+---+----+----------+------------+----------+
## |conc_rainfall    |No | 18|Inf |-0.4519851|-2.833213344|      -Inf|
## |                 |Yes|511|Inf | 2.5797959| 0.058725286|-1.8740621|
## +-----------------+---+---+----+----------+------------+----------+
## |wastewater       |No |206|Inf | 1.6211340|-0.474739234|-2.6977408|
## |                 |Yes|323|Inf | 3.1716229| 0.293102140|-1.5836538|
## +-----------------+---+---+----+----------+------------+----------+
## |ground_veg       |No |160|Inf | 1.3862944|-1.132228899|-2.5123056|
## |                 |Yes|369|Inf | 3.1612467| 0.446287103|-1.7208515|
## +-----------------+---+---+----+----------+------------+----------+
## |angle            |C  | 28|Inf |       Inf| 0.000000000|-3.2958369|
## |                 |D  |125|Inf | 3.7054088| 0.905117431|-1.1093076|
## |                 |E  |376|Inf | 2.0209453|-0.289233663|-2.2454267|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_saprolite_soil|No |444|Inf | 2.2587825|-0.108213585|-2.0200181|
## |                 |Yes| 85|Inf | 2.7725887| 0.554996842|-1.4615178|
## +-----------------+---+---+----+----------+------------+----------+
## |Overall          |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +-----------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=1, cex.sub=1)

Equation 2

parameters okay and so/so
porportion
excluded TC_geol_desf

f2 <- lrm(risk ~ angle + building + EN + TC_saprolito + Taterro + DepEncNatural + DepTaludeAterro + DepTaludeCorte + landfill + garbage + construction_deposit + crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + leak + drainage + TC_mature_Soil + density + TC + tree +ground_veg + deforestation + banana , data=train.data, x=TRUE , y=TRUE)

      stargazer(anova(f2), type="text", style="default")

eq_OLR_02 <- polr(risk ~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,
                  
                 data= train.data
           ,  method = "logistic", Hess = TRUE)

## Warning: glm.fit: fitted probabilities numerically 0 or 1 occurred

ctable <- coef(summary(eq_OLR_02))








p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                             Value Std. Error     t value      p value
## brickTRUE             -0.93805399  0.5306767 -1.76765616 3.855920e-02
## woodTRUE               1.13425624  0.3554699  3.19086394 7.092403e-04
## ENTRUE                 0.47872035  0.3931647  1.21760766 1.116866e-01
## TC_mature_soilTRUE     0.48649743  0.2272782  2.14053737 1.615568e-02
## T_constructionTRUE     0.71920921  0.3722120  1.93225707 2.666389e-02
## landfillTRUE          -0.24902235  0.3386661 -0.73530344 2.310774e-01
## leakTRUE              -0.30305829  0.2409441 -1.25779483 1.042330e-01
## garbageTRUE           -0.21516738  0.3014620 -0.71374626 2.376920e-01
## crackTRUE              2.01100798  0.3371095  5.96544490 1.219842e-09
## leaning_wallTRUE       2.03501252  0.5295070  3.84322135 6.071493e-05
## treeTRUE              -0.16913426  0.2486148 -0.68030659 2.481552e-01
## downward_floorTRUE     1.15360836  0.3836200  3.00716419 1.318486e-03
## tiltedTRUE             0.96515938  0.3182641  3.03257396 1.212388e-03
## ground_vegTRUE         0.65899757  0.2690825  2.44905383 7.161602e-03
## scarsTRUE              3.76699390  0.3475902 10.83745672 1.143709e-27
## mixedTRUE              0.02471816  0.5172665  0.04778612 4.809433e-01
## conc_rainfallTRUE      1.45300464  0.5730475  2.53557459 5.613151e-03
## wastewaterTRUE         0.45039320  0.2401050  1.87581794 3.034014e-02
## angleD                 0.18570674  0.4842897  0.38346208 3.506886e-01
## angleE                 0.06641678  0.5620605  0.11816660 4.529678e-01
## bananaTRUE             0.36261636  0.2601767  1.39373101 8.169942e-02
## drainage.L             0.80107184  0.2779955  2.88159971 1.978310e-03
## drainage.Q            -0.15990284  0.1863207 -0.85821279 1.953875e-01
## TC_saprolite_soilTRUE  0.02033632  0.2997866  0.06783600 4.729581e-01
## TCTRUE                -0.19466922  0.5346406 -0.36411231 3.578871e-01
## deforestationTRUE      0.39194172  0.3899147  1.00519859 1.574006e-01
## R1|R2                 -0.43529380  1.1292570 -0.38546920 3.499449e-01
## R2|R3                  3.92596552  1.1491351  3.41645239 3.172138e-04
## R3|R4                  8.97054482  1.2232004  7.33366754 1.119691e-13

stargazer((ctable), type="text", style="default", digits=2)

## 
## ======================================================
##                       Value Std. Error t value p value
## ------------------------------------------------------
## brickTRUE             -0.94    0.53     -1.77   0.04  
## woodTRUE              1.13     0.36     3.19    0.001 
## ENTRUE                0.48     0.39     1.22    0.11  
## TC_mature_soilTRUE    0.49     0.23     2.14    0.02  
## T_constructionTRUE    0.72     0.37     1.93    0.03  
## landfillTRUE          -0.25    0.34     -0.74   0.23  
## leakTRUE              -0.30    0.24     -1.26   0.10  
## garbageTRUE           -0.22    0.30     -0.71   0.24  
## crackTRUE             2.01     0.34     5.97      0   
## leaning_wallTRUE      2.04     0.53     3.84   0.0001 
## treeTRUE              -0.17    0.25     -0.68   0.25  
## downward_floorTRUE    1.15     0.38     3.01    0.001 
## tiltedTRUE            0.97     0.32     3.03    0.001 
## ground_vegTRUE        0.66     0.27     2.45    0.01  
## scarsTRUE             3.77     0.35     10.84     0   
## mixedTRUE             0.02     0.52     0.05    0.48  
## conc_rainfallTRUE     1.45     0.57     2.54    0.01  
## wastewaterTRUE        0.45     0.24     1.88    0.03  
## angleD                0.19     0.48     0.38    0.35  
## angleE                0.07     0.56     0.12    0.45  
## bananaTRUE            0.36     0.26     1.39    0.08  
## drainage.L            0.80     0.28     2.88    0.002 
## drainage.Q            -0.16    0.19     -0.86   0.20  
## TC_saprolite_soilTRUE 0.02     0.30     0.07    0.47  
## TCTRUE                -0.19    0.53     -0.36   0.36  
## deforestationTRUE     0.39     0.39     1.01    0.16  
## R1| R2                -0.44    1.13     -0.39   0.35  
## R2| R3                3.93     1.15     3.42   0.0003 
## R3| R4                8.97     1.22     7.33      0   
## ------------------------------------------------------

par(mfrow=c(6,4))
plot.xmean.ordinaly (risk~ brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation
          ,data=train.data, cr=TRUE , subn=FALSE ,  cex.lab=1.5, cex.axis=4, cex.sub=4, cex.main=4)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ EN+  TC_mature_soil+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ mixed+ conc_rainfall+ wastewater+ angle+ banana+ drainage+ TC_saprolite_soil+ TC+ deforestation,data=train.data
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +-----------------+---+---+----+----------+------------+----------+
## |                 |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +-----------------+---+---+----+----------+------------+----------+
## |brick            |No | 36|Inf | 2.8332133| 1.609437912|-0.4519851|
## |                 |Yes|493|Inf | 2.2981307|-0.101506946|-2.0955154|
## +-----------------+---+---+----+----------+------------+----------+
## |wood             |No |455|Inf | 2.2094947|-0.198450939|-2.2598232|
## |                 |Yes| 74|Inf | 3.5835189| 1.369487243|-0.6729445|
## +-----------------+---+---+----+----------+------------+----------+
## |EN               |No |348|Inf | 1.9070703|-0.443931389|-2.4756043|
## |                 |Yes|181|Inf | 4.4942386| 0.881738350|-1.2280704|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_mature_soil   |No |260|Inf | 1.8941745|-0.185142433|-2.2407097|
## |                 |Yes|269|Inf | 2.9802281| 0.171422266|-1.6593349|
## +-----------------+---+---+----+----------+------------+----------+
## |T_construction   |No |224|Inf | 1.5567942|-0.894516932|-3.1734135|
## |                 |Yes|305|Inf | 3.6142906| 0.629865731|-1.4277941|
## +-----------------+---+---+----+----------+------------+----------+
## |landfill         |No |335|Inf | 1.8632184|-0.493350993|-2.6075090|
## |                 |Yes|194|Inf | 4.5643482| 0.876929658|-1.1972838|
## +-----------------+---+---+----+----------+------------+----------+
## |leak             |No |345|Inf | 1.9760632|-0.297834444|-2.4266973|
## |                 |Yes|184|Inf | 3.5779479| 0.557481315|-1.2809338|
## +-----------------+---+---+----+----------+------------+----------+
## |garbage          |No |357|Inf | 2.0421701|-0.299129458|-2.3529102|
## |                 |Yes|172|Inf | 3.3202283| 0.624154309|-1.2943569|
## +-----------------+---+---+----+----------+------------+----------+
## |crack            |No |444|Inf | 2.1822989|-0.317967468|-2.6246686|
## |                 |Yes| 85|Inf | 3.7256934| 2.264363880|-0.2125614|
## +-----------------+---+---+----+----------+------------+----------+
## |leaning_wall     |No |497|Inf | 2.2591000|-0.132992454|-2.1905356|
## |                 |Yes| 32|Inf |       Inf|         Inf| 0.2513144|
## +-----------------+---+---+----+----------+------------+----------+
## |tree             |No |202|Inf | 1.7076764|-0.547965171|-2.2650472|
## |                 |Yes|327|Inf | 2.9672042| 0.327043146|-1.7358008|
## +-----------------+---+---+----+----------+------------+----------+
## |downward_floor   |No |473|Inf | 2.2042917|-0.225046501|-2.2524607|
## |                 |Yes| 56|Inf |       Inf| 3.295836866|-0.3610133|
## +-----------------+---+---+----+----------+------------+----------+
## |tilted           |No |434|Inf | 2.1082771|-0.392042088|-2.4336134|
## |                 |Yes| 95|Inf |       Inf| 2.696876901|-0.6306268|
## +-----------------+---+---+----+----------+------------+----------+
## |ground_veg       |No |160|Inf | 1.3862944|-1.132228899|-2.5123056|
## |                 |Yes|369|Inf | 3.1612467| 0.446287103|-1.7208515|
## +-----------------+---+---+----+----------+------------+----------+
## |scars            |No |319|Inf | 1.7808304|-1.421941700|-4.1399551|
## |                 |Yes|210|Inf | 5.3423343| 3.228826156|-0.8472979|
## +-----------------+---+---+----+----------+------------+----------+
## |mixed            |No |492|Inf | 2.2958961|-0.065063593|-1.9740810|
## |                 |Yes| 37|Inf | 2.8622009| 0.860201265|-1.2878543|
## +-----------------+---+---+----+----------+------------+----------+
## |conc_rainfall    |No | 18|Inf |-0.4519851|-2.833213344|      -Inf|
## |                 |Yes|511|Inf | 2.5797959| 0.058725286|-1.8740621|
## +-----------------+---+---+----+----------+------------+----------+
## |wastewater       |No |206|Inf | 1.6211340|-0.474739234|-2.6977408|
## |                 |Yes|323|Inf | 3.1716229| 0.293102140|-1.5836538|
## +-----------------+---+---+----+----------+------------+----------+
## |angle            |C  | 28|Inf |       Inf| 0.000000000|-3.2958369|
## |                 |D  |125|Inf | 3.7054088| 0.905117431|-1.1093076|
## |                 |E  |376|Inf | 2.0209453|-0.289233663|-2.2454267|
## +-----------------+---+---+----+----------+------------+----------+
## |banana           |No |360|Inf | 1.9459101|-0.359374001|-2.1972246|
## |                 |Yes|169|Inf | 4.4248466| 0.783298278|-1.4542450|
## +-----------------+---+---+----+----------+------------+----------+
## |drainage         |Y  | 70|Inf | 0.7801586|-1.575536361|-3.5263605|
## |                 |P  |240|Inf | 2.4537237|-0.546543706|-2.3978953|
## |                 |N  |219|Inf | 3.5695327| 1.092533243|-1.3246502|
## +-----------------+---+---+----+----------+------------+----------+
## |TC_saprolite_soil|No |444|Inf | 2.2587825|-0.108213585|-2.0200181|
## |                 |Yes| 85|Inf | 2.7725887| 0.554996842|-1.4615178|
## +-----------------+---+---+----+----------+------------+----------+
## |TC               |No | 26|Inf |       Inf| 0.810930216|-1.2039728|
## |                 |Yes|503|Inf | 2.2723452|-0.043744549|-1.9619105|
## +-----------------+---+---+----+----------+------------+----------+
## |deforestation    |No |488|Inf | 2.3368742| 0.049190244|-1.8729849|
## |                 |Yes| 41|Inf | 2.2246236|-0.656779536|-2.5389739|
## +-----------------+---+---+----+----------+------------+----------+
## |Overall          |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +-----------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=1, cex.axis=2, cex.sub=1)

Equation 3

parameters okay and so/so
porportion
p-value based equation 2 > 0.5

f3 <- lrm(risk ~ angle +building + EN + DepTaludeAterro+ DepTaludeCorte+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall_water+ wastewater+ tree + TC , data=train.data, x=TRUE , y=TRUE) stargazer(anova(f3), type=“text”, style=“default”)

# x=TRUE, y=TRUE used by resid() below 

eq_OLR_03 <- polr(risk ~ wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, data=train.data
           ,  method = "logistic", Hess = TRUE)
ctable <- coef(summary(eq_OLR_03))


p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error    t value      p value
## woodTRUE            1.16443202  0.3344192  3.4819534 2.488852e-04
## TC_mature_soilTRUE  0.48039422  0.2161040  2.2229774 1.310866e-02
## T_constructionTRUE  0.48516262  0.2932156  1.6546277 4.900002e-02
## landfillTRUE       -0.02320693  0.2972534 -0.0780712 4.688857e-01
## crackTRUE           1.98729825  0.3268080  6.0809351 5.974183e-10
## leaning_wallTRUE    2.07965826  0.5270628  3.9457507 3.977517e-05
## treeTRUE           -0.11878902  0.2387166 -0.4976152 3.093777e-01
## downward_floorTRUE  1.08473106  0.3681854  2.9461541 1.608760e-03
## tiltedTRUE          1.00420680  0.3130299  3.2080223 6.682557e-04
## ground_vegTRUE      0.68821424  0.2615814  2.6309755 4.257008e-03
## scarsTRUE           3.68891379  0.3410446 10.8165134 1.437538e-27
## conc_rainfallTRUE   1.49896469  0.5672627  2.6424526 4.115399e-03
## wastewaterTRUE      0.43939795  0.2328797  1.8868019 2.959349e-02
## bananaTRUE          0.41640892  0.2496973  1.6676549 4.769213e-02
## drainage.L          0.79948756  0.2719775  2.9395356 1.643523e-03
## drainage.Q         -0.11590723  0.1839551 -0.6300843 2.643197e-01
## R1|R2               0.63406774  0.5577303  1.1368717 1.277960e-01
## R2|R3               4.89779715  0.6155953  7.9561959 8.870470e-16
## R3|R4               9.83361613  0.7514390 13.0863789 1.969728e-39

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           1.16     0.33     3.48   0.0002 
## TC_mature_soilTRUE 0.48     0.22     2.22    0.01  
## T_constructionTRUE 0.49     0.29     1.65    0.05  
## landfillTRUE       -0.02    0.30     -0.08   0.47  
## crackTRUE          1.99     0.33     6.08      0   
## leaning_wallTRUE   2.08     0.53     3.95   0.0000 
## treeTRUE           -0.12    0.24     -0.50   0.31  
## downward_floorTRUE 1.08     0.37     2.95    0.002 
## tiltedTRUE         1.00     0.31     3.21    0.001 
## ground_vegTRUE     0.69     0.26     2.63    0.004 
## scarsTRUE          3.69     0.34     10.82     0   
## conc_rainfallTRUE  1.50     0.57     2.64    0.004 
## wastewaterTRUE     0.44     0.23     1.89    0.03  
## bananaTRUE         0.42     0.25     1.67    0.05  
## drainage.L         0.80     0.27     2.94    0.002 
## drainage.Q         -0.12    0.18     -0.63   0.26  
## R1| R2             0.63     0.56     1.14    0.13  
## R2| R3             4.90     0.62     7.96      0   
## R3| R4             9.83     0.75     13.09     0   
## ---------------------------------------------------

#print (f3 , latex =TRUE , coefs =5)

par(mfrow=c(3,5))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage,,
          data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ landfill+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |455|Inf | 2.2094947|-0.198450939|-2.2598232|
## |              |Yes| 74|Inf | 3.5835189| 1.369487243|-0.6729445|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |260|Inf | 1.8941745|-0.185142433|-2.2407097|
## |              |Yes|269|Inf | 2.9802281| 0.171422266|-1.6593349|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |224|Inf | 1.5567942|-0.894516932|-3.1734135|
## |              |Yes|305|Inf | 3.6142906| 0.629865731|-1.4277941|
## +--------------+---+---+----+----------+------------+----------+
## |landfill      |No |335|Inf | 1.8632184|-0.493350993|-2.6075090|
## |              |Yes|194|Inf | 4.5643482| 0.876929658|-1.1972838|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |444|Inf | 2.1822989|-0.317967468|-2.6246686|
## |              |Yes| 85|Inf | 3.7256934| 2.264363880|-0.2125614|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.132992454|-2.1905356|
## |              |Yes| 32|Inf |       Inf|         Inf| 0.2513144|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |202|Inf | 1.7076764|-0.547965171|-2.2650472|
## |              |Yes|327|Inf | 2.9672042| 0.327043146|-1.7358008|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |473|Inf | 2.2042917|-0.225046501|-2.2524607|
## |              |Yes| 56|Inf |       Inf| 3.295836866|-0.3610133|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |434|Inf | 2.1082771|-0.392042088|-2.4336134|
## |              |Yes| 95|Inf |       Inf| 2.696876901|-0.6306268|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |160|Inf | 1.3862944|-1.132228899|-2.5123056|
## |              |Yes|369|Inf | 3.1612467| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |319|Inf | 1.7808304|-1.421941700|-4.1399551|
## |              |Yes|210|Inf | 5.3423343| 3.228826156|-0.8472979|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 18|Inf |-0.4519851|-2.833213344|      -Inf|
## |              |Yes|511|Inf | 2.5797959| 0.058725286|-1.8740621|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |206|Inf | 1.6211340|-0.474739234|-2.6977408|
## |              |Yes|323|Inf | 3.1716229| 0.293102140|-1.5836538|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |360|Inf | 1.9459101|-0.359374001|-2.1972246|
## |              |Yes|169|Inf | 4.4248466| 0.783298278|-1.4542450|
## +--------------+---+---+----+----------+------------+----------+
## |drainage      |Y  | 70|Inf | 0.7801586|-1.575536361|-3.5263605|
## |              |P  |240|Inf | 2.4537237|-0.546543706|-2.3978953|
## |              |N  |219|Inf | 3.5695327| 1.092533243|-1.3246502|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.6, cex.axis=0.6, cex.sub=0.6)

Equation 4

p-value equation 3 > 0.05 (banana, DepTaludeCorte)
consider proportion
paremeters okay & so/so
fashion order

f4 <- lrm(risk ~ building + EN
+ DepEncNatural
+ crack + leaning_wall + scars + downward_floor +tilted + conc_rainfall_water + wastewater + drainage + TC_mature_Soil + TC + +ground_veg
,data=train.data, x=TRUE , y=TRUE) # x=TRUE, y=TRUE used by resid() below #print (f4 , latex =TRUE , coefs =5) stargazer(anova(f4), type=“text”, style=“default”)

eq_OLR_04 <- polr(risk~ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
                  , data= train.data
           ,  method = "logistic", Hess = TRUE)
p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value

ctable <- coef(summary(eq_OLR_04))

ctable <- cbind(ctable, "p value" = p )

## Warning in cbind(ctable, `p value` = p): number of rows of result is not a
## multiple of vector length (arg 2)

ctable

##                         Value Std. Error    t value      p value
## woodTRUE            1.1644451  0.3344545  3.4816251 2.488852e-04
## TC_mature_soilTRUE  0.4820550  0.2150829  2.2412525 1.310866e-02
## T_constructionTRUE  0.4713918  0.2343514  2.0114746 4.900002e-02
## crackTRUE           1.9838718  0.3238415  6.1260585 4.688857e-01
## leaning_wallTRUE    2.0811566  0.5265700  3.9522888 5.974183e-10
## treeTRUE           -0.1165493  0.2369379 -0.4918982 3.977517e-05
## downward_floorTRUE  1.0817894  0.3662799  2.9534501 3.093777e-01
## tiltedTRUE          0.9989767  0.3058044  3.2667183 1.608760e-03
## ground_vegTRUE      0.6860575  0.2600567  2.6381069 6.682557e-04
## scarsTRUE           3.6891713  0.3409953 10.8188338 4.257008e-03
## conc_rainfallTRUE   1.4989010  0.5671314  2.6429519 1.437538e-27
## wastewaterTRUE      0.4424641  0.2295765  1.9273062 4.115399e-03
## bananaTRUE          0.4162586  0.2496792  1.6671735 2.959349e-02
## drainage.L          0.7987598  0.2718212  2.9385486 4.769213e-02
## drainage.Q         -0.1157995  0.1839428 -0.6295407 1.643523e-03
## R1|R2               0.6353627  0.5572983  1.1400764 2.643197e-01
## R2|R3               4.8989925  0.6152733  7.9623036 1.277960e-01
## R3|R4               9.8351326  0.7510472 13.0952255 8.870470e-16

stargazer((ctable), type="text", style="default", digits=2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## woodTRUE           1.16     0.33     3.48   0.0002 
## TC_mature_soilTRUE 0.48     0.22     2.24    0.01  
## T_constructionTRUE 0.47     0.23     2.01    0.05  
## crackTRUE          1.98     0.32     6.13    0.47  
## leaning_wallTRUE   2.08     0.53     3.95      0   
## treeTRUE           -0.12    0.24     -0.49  0.0000 
## downward_floorTRUE 1.08     0.37     2.95    0.31  
## tiltedTRUE         1.00     0.31     3.27    0.002 
## ground_vegTRUE     0.69     0.26     2.64    0.001 
## scarsTRUE          3.69     0.34     10.82   0.004 
## conc_rainfallTRUE  1.50     0.57     2.64      0   
## wastewaterTRUE     0.44     0.23     1.93    0.004 
## bananaTRUE         0.42     0.25     1.67    0.03  
## drainage.L         0.80     0.27     2.94    0.05  
## drainage.Q         -0.12    0.18     -0.63   0.002 
## R1| R2             0.64     0.56     1.14    0.26  
## R2| R3             4.90     0.62     7.96    0.13  
## R3| R4             9.84     0.75     13.10     0   
## ---------------------------------------------------

par(mfrow=c(4,4))
plot.xmean.ordinaly (risk ~  wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ tree+ downward_floor+ tilted+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana+ drainage
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |455|Inf | 2.2094947|-0.198450939|-2.2598232|
## |              |Yes| 74|Inf | 3.5835189| 1.369487243|-0.6729445|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |260|Inf | 1.8941745|-0.185142433|-2.2407097|
## |              |Yes|269|Inf | 2.9802281| 0.171422266|-1.6593349|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |224|Inf | 1.5567942|-0.894516932|-3.1734135|
## |              |Yes|305|Inf | 3.6142906| 0.629865731|-1.4277941|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |444|Inf | 2.1822989|-0.317967468|-2.6246686|
## |              |Yes| 85|Inf | 3.7256934| 2.264363880|-0.2125614|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.132992454|-2.1905356|
## |              |Yes| 32|Inf |       Inf|         Inf| 0.2513144|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |202|Inf | 1.7076764|-0.547965171|-2.2650472|
## |              |Yes|327|Inf | 2.9672042| 0.327043146|-1.7358008|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |473|Inf | 2.2042917|-0.225046501|-2.2524607|
## |              |Yes| 56|Inf |       Inf| 3.295836866|-0.3610133|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |434|Inf | 2.1082771|-0.392042088|-2.4336134|
## |              |Yes| 95|Inf |       Inf| 2.696876901|-0.6306268|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |160|Inf | 1.3862944|-1.132228899|-2.5123056|
## |              |Yes|369|Inf | 3.1612467| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |319|Inf | 1.7808304|-1.421941700|-4.1399551|
## |              |Yes|210|Inf | 5.3423343| 3.228826156|-0.8472979|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 18|Inf |-0.4519851|-2.833213344|      -Inf|
## |              |Yes|511|Inf | 2.5797959| 0.058725286|-1.8740621|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |206|Inf | 1.6211340|-0.474739234|-2.6977408|
## |              |Yes|323|Inf | 3.1716229| 0.293102140|-1.5836538|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |360|Inf | 1.9459101|-0.359374001|-2.1972246|
## |              |Yes|169|Inf | 4.4248466| 0.783298278|-1.4542450|
## +--------------+---+---+----+----------+------------+----------+
## |drainage      |Y  | 70|Inf | 0.7801586|-1.575536361|-3.5263605|
## |              |P  |240|Inf | 2.4537237|-0.546543706|-2.3978953|
## |              |N  |219|Inf | 3.5695327| 1.092533243|-1.3246502|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Equation 5 - Based on Equation 1

based on Eq 1
less p-value > 0.10 (

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_05 <- polr(risk ~ brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_05))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                         Value Std. Error    t value      p value
## brickTRUE          -0.9154798  0.4407467 -2.0771112 1.889565e-02
## woodTRUE            1.2187649  0.3324982  3.6654787 1.234382e-04
## TC_mature_soilTRUE  0.4761820  0.2140928  2.2241851 1.306800e-02
## T_constructionTRUE  0.5470797  0.2300691  2.3778927 8.705947e-03
## crackTRUE           1.9307592  0.3201583  6.0306395 8.165607e-10
## leaning_wallTRUE    1.8500694  0.5235969  3.5333847 2.051375e-04
## scarsTRUE           3.7625502  0.3395457 11.0811308 7.745184e-29
## downward_floorTRUE  1.1884288  0.3690203  3.2204975 6.398415e-04
## tiltedTRUE          1.1103260  0.3068371  3.6186172 1.480907e-04
## conc_rainfallTRUE   1.9000278  0.5367538  3.5398494 2.001777e-04
## wastewaterTRUE      0.5432397  0.2235931  2.4295902 7.557952e-03
## ground_vegTRUE      0.8479276  0.2345053  3.6158137 1.497028e-04
## R1|R2               0.2825837  0.6838108  0.4132483 3.397123e-01
## R2|R3               4.4183960  0.7364967  5.9992066 9.914200e-10
## R3|R4               9.3567928  0.8350943 11.2044752 1.938222e-29

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -0.92    0.44     -2.08   0.02  
## woodTRUE           1.22     0.33     3.67   0.0001 
## TC_mature_soilTRUE 0.48     0.21     2.22    0.01  
## T_constructionTRUE 0.55     0.23     2.38    0.01  
## crackTRUE          1.93     0.32     6.03      0   
## leaning_wallTRUE   1.85     0.52     3.53   0.0002 
## scarsTRUE          3.76     0.34     11.08     0   
## downward_floorTRUE 1.19     0.37     3.22    0.001 
## tiltedTRUE         1.11     0.31     3.62   0.0001 
## conc_rainfallTRUE  1.90     0.54     3.54   0.0002 
## wastewaterTRUE     0.54     0.22     2.43    0.01  
## ground_vegTRUE     0.85     0.23     3.62   0.0001 
## R1| R2             0.28     0.68     0.41    0.34  
## R2| R3             4.42     0.74     6.00      0   
## R3| R4             9.36     0.84     11.20     0   
## ---------------------------------------------------

par(mfrow=c(3,4))
plot.xmean.ordinaly (risk ~  brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+  TC_mature_soil+ T_construction+ crack+ leaning_wall+ scars+ downward_floor+ tilted+ conc_rainfall+ wastewater+ ground_veg
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |brick         |No | 36|Inf | 2.8332133| 1.609437912|-0.4519851|
## |              |Yes|493|Inf | 2.2981307|-0.101506946|-2.0955154|
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |455|Inf | 2.2094947|-0.198450939|-2.2598232|
## |              |Yes| 74|Inf | 3.5835189| 1.369487243|-0.6729445|
## +--------------+---+---+----+----------+------------+----------+
## |TC_mature_soil|No |260|Inf | 1.8941745|-0.185142433|-2.2407097|
## |              |Yes|269|Inf | 2.9802281| 0.171422266|-1.6593349|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |224|Inf | 1.5567942|-0.894516932|-3.1734135|
## |              |Yes|305|Inf | 3.6142906| 0.629865731|-1.4277941|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |444|Inf | 2.1822989|-0.317967468|-2.6246686|
## |              |Yes| 85|Inf | 3.7256934| 2.264363880|-0.2125614|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.132992454|-2.1905356|
## |              |Yes| 32|Inf |       Inf|         Inf| 0.2513144|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |319|Inf | 1.7808304|-1.421941700|-4.1399551|
## |              |Yes|210|Inf | 5.3423343| 3.228826156|-0.8472979|
## +--------------+---+---+----+----------+------------+----------+
## |downward_floor|No |473|Inf | 2.2042917|-0.225046501|-2.2524607|
## |              |Yes| 56|Inf |       Inf| 3.295836866|-0.3610133|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |434|Inf | 2.1082771|-0.392042088|-2.4336134|
## |              |Yes| 95|Inf |       Inf| 2.696876901|-0.6306268|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 18|Inf |-0.4519851|-2.833213344|      -Inf|
## |              |Yes|511|Inf | 2.5797959| 0.058725286|-1.8740621|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |206|Inf | 1.6211340|-0.474739234|-2.6977408|
## |              |Yes|323|Inf | 3.1716229| 0.293102140|-1.5836538|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |160|Inf | 1.3862944|-1.132228899|-2.5123056|
## |              |Yes|369|Inf | 3.1612467| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

OLR Equation 6

# x=TRUE, y=TRUE used by resid() below 
#print (f1 , latex =TRUE , coefs =5)
#stargazer(anova(f1), type="text", style="default")

eq_OLR_06 <- polr(risk ~ brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana,  data= train.data
           ,  method = "logistic", Hess = TRUE)

ctable <- coef(summary(eq_OLR_06))

p<- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE*2) #computes p value
ctable <- cbind(ctable, "p value" = p )

ctable

##                          Value Std. Error    t value      p value
## brickTRUE          -1.12193398  0.5161824 -2.1735222 1.487052e-02
## woodTRUE            1.07366206  0.3356084  3.1991514 6.891638e-04
## mixedTRUE           0.33992301  0.4973194  0.6835104 2.471422e-01
## ENTRUE              0.49216761  0.3838805  1.2820855 9.990632e-02
## TCTRUE              0.18875929  0.5051849  0.3736440 3.543346e-01
## T_constructionTRUE  0.58652586  0.3569660  1.6430861 5.018256e-02
## landfillTRUE       -0.06970011  0.3262988 -0.2136082 4.154263e-01
## leakTRUE           -0.08885997  0.2305788 -0.3853778 3.499787e-01
## garbageTRUE        -0.08288739  0.2925339 -0.2833429 3.884570e-01
## crackTRUE           1.90086296  0.3284512  5.7873536 3.575199e-09
## leaning_wallTRUE    2.04340641  0.5273765  3.8746635 5.338607e-05
## treeTRUE           -0.17275809  0.2419904 -0.7139046 2.376431e-01
## tiltedTRUE          1.09092737  0.3107130  3.5110450 2.231744e-04
## angleD              0.22193237  0.4743440  0.4678722 3.199380e-01
## angleE              0.10377558  0.5489883  0.1890306 4.250344e-01
## ground_vegTRUE      0.71814526  0.2606371  2.7553452 2.931512e-03
## scarsTRUE           3.87149244  0.3454210 11.2080408 1.861720e-29
## conc_rainfallTRUE   2.03442386  0.5391154  3.7736333 8.044360e-05
## wastewaterTRUE      0.55271565  0.2300166  2.4029384 8.131963e-03
## bananaTRUE          0.51404490  0.2537603  2.0257103 2.139724e-02
## R1|R2               0.28510663  1.0709128  0.2662277 3.950319e-01
## R2|R3               4.41925742  1.1004173  4.0159833 2.959922e-05
## R3|R4               9.37573715  1.1775912  7.9617931 8.478190e-16

stargazer((ctable), type="text", style="default", digits = 2)

## 
## ===================================================
##                    Value Std. Error t value p value
## ---------------------------------------------------
## brickTRUE          -1.12    0.52     -2.17   0.01  
## woodTRUE           1.07     0.34     3.20    0.001 
## mixedTRUE          0.34     0.50     0.68    0.25  
## ENTRUE             0.49     0.38     1.28    0.10  
## TCTRUE             0.19     0.51     0.37    0.35  
## T_constructionTRUE 0.59     0.36     1.64    0.05  
## landfillTRUE       -0.07    0.33     -0.21   0.42  
## leakTRUE           -0.09    0.23     -0.39   0.35  
## garbageTRUE        -0.08    0.29     -0.28   0.39  
## crackTRUE          1.90     0.33     5.79      0   
## leaning_wallTRUE   2.04     0.53     3.87   0.0001 
## treeTRUE           -0.17    0.24     -0.71   0.24  
## tiltedTRUE         1.09     0.31     3.51   0.0002 
## angleD             0.22     0.47     0.47    0.32  
## angleE             0.10     0.55     0.19    0.43  
## ground_vegTRUE     0.72     0.26     2.76    0.003 
## scarsTRUE          3.87     0.35     11.21     0   
## conc_rainfallTRUE  2.03     0.54     3.77   0.0001 
## wastewaterTRUE     0.55     0.23     2.40    0.01  
## bananaTRUE         0.51     0.25     2.03    0.02  
## R1| R2             0.29     1.07     0.27    0.40  
## R2| R3             4.42     1.10     4.02   0.0000 
## R3| R4             9.38     1.18     7.96      0   
## ---------------------------------------------------

par(mfrow=c(5,4))
plot.xmean.ordinaly (risk ~  brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
          ,data=train.data, cr=TRUE , subn=FALSE , cex.points =0.65)

sf <- function (y) {
        c('y>=1' = qlogis(mean(y>=1)),
          'y>=2' = qlogis(mean(y>=2)),
           'y>=3' = qlogis(mean(y>=3)),
            'y>=4' = qlogis(mean(y>=4)))
}

s<-with(train.data, summary(as.numeric(risk)~brick+ wood+ mixed+ EN+ TC+ T_construction+ landfill+ leak+ garbage+ crack+ leaning_wall+ tree+ tilted+ angle+ ground_veg+ scars+ conc_rainfall+ wastewater+ banana
, fun=sf))
s

## as.numeric(risk)     N= 529 , 1 Missing 
## 
## +--------------+---+---+----+----------+------------+----------+
## |              |   |N  |y>=1|y>=2      |y>=3        |y>=4      |
## +--------------+---+---+----+----------+------------+----------+
## |brick         |No | 36|Inf | 2.8332133| 1.609437912|-0.4519851|
## |              |Yes|493|Inf | 2.2981307|-0.101506946|-2.0955154|
## +--------------+---+---+----+----------+------------+----------+
## |wood          |No |455|Inf | 2.2094947|-0.198450939|-2.2598232|
## |              |Yes| 74|Inf | 3.5835189| 1.369487243|-0.6729445|
## +--------------+---+---+----+----------+------------+----------+
## |mixed         |No |492|Inf | 2.2958961|-0.065063593|-1.9740810|
## |              |Yes| 37|Inf | 2.8622009| 0.860201265|-1.2878543|
## +--------------+---+---+----+----------+------------+----------+
## |EN            |No |348|Inf | 1.9070703|-0.443931389|-2.4756043|
## |              |Yes|181|Inf | 4.4942386| 0.881738350|-1.2280704|
## +--------------+---+---+----+----------+------------+----------+
## |TC            |No | 26|Inf |       Inf| 0.810930216|-1.2039728|
## |              |Yes|503|Inf | 2.2723452|-0.043744549|-1.9619105|
## +--------------+---+---+----+----------+------------+----------+
## |T_construction|No |224|Inf | 1.5567942|-0.894516932|-3.1734135|
## |              |Yes|305|Inf | 3.6142906| 0.629865731|-1.4277941|
## +--------------+---+---+----+----------+------------+----------+
## |landfill      |No |335|Inf | 1.8632184|-0.493350993|-2.6075090|
## |              |Yes|194|Inf | 4.5643482| 0.876929658|-1.1972838|
## +--------------+---+---+----+----------+------------+----------+
## |leak          |No |345|Inf | 1.9760632|-0.297834444|-2.4266973|
## |              |Yes|184|Inf | 3.5779479| 0.557481315|-1.2809338|
## +--------------+---+---+----+----------+------------+----------+
## |garbage       |No |357|Inf | 2.0421701|-0.299129458|-2.3529102|
## |              |Yes|172|Inf | 3.3202283| 0.624154309|-1.2943569|
## +--------------+---+---+----+----------+------------+----------+
## |crack         |No |444|Inf | 2.1822989|-0.317967468|-2.6246686|
## |              |Yes| 85|Inf | 3.7256934| 2.264363880|-0.2125614|
## +--------------+---+---+----+----------+------------+----------+
## |leaning_wall  |No |497|Inf | 2.2591000|-0.132992454|-2.1905356|
## |              |Yes| 32|Inf |       Inf|         Inf| 0.2513144|
## +--------------+---+---+----+----------+------------+----------+
## |tree          |No |202|Inf | 1.7076764|-0.547965171|-2.2650472|
## |              |Yes|327|Inf | 2.9672042| 0.327043146|-1.7358008|
## +--------------+---+---+----+----------+------------+----------+
## |tilted        |No |434|Inf | 2.1082771|-0.392042088|-2.4336134|
## |              |Yes| 95|Inf |       Inf| 2.696876901|-0.6306268|
## +--------------+---+---+----+----------+------------+----------+
## |angle         |C  | 28|Inf |       Inf| 0.000000000|-3.2958369|
## |              |D  |125|Inf | 3.7054088| 0.905117431|-1.1093076|
## |              |E  |376|Inf | 2.0209453|-0.289233663|-2.2454267|
## +--------------+---+---+----+----------+------------+----------+
## |ground_veg    |No |160|Inf | 1.3862944|-1.132228899|-2.5123056|
## |              |Yes|369|Inf | 3.1612467| 0.446287103|-1.7208515|
## +--------------+---+---+----+----------+------------+----------+
## |scars         |No |319|Inf | 1.7808304|-1.421941700|-4.1399551|
## |              |Yes|210|Inf | 5.3423343| 3.228826156|-0.8472979|
## +--------------+---+---+----+----------+------------+----------+
## |conc_rainfall |No | 18|Inf |-0.4519851|-2.833213344|      -Inf|
## |              |Yes|511|Inf | 2.5797959| 0.058725286|-1.8740621|
## +--------------+---+---+----+----------+------------+----------+
## |wastewater    |No |206|Inf | 1.6211340|-0.474739234|-2.6977408|
## |              |Yes|323|Inf | 3.1716229| 0.293102140|-1.5836538|
## +--------------+---+---+----+----------+------------+----------+
## |banana        |No |360|Inf | 1.9459101|-0.359374001|-2.1972246|
## |              |Yes|169|Inf | 4.4248466| 0.783298278|-1.4542450|
## +--------------+---+---+----+----------+------------+----------+
## |Overall       |   |529|Inf | 2.3277965|-0.003780723|-1.9138903|
## +--------------+---+---+----+----------+------------+----------+

plot(s, which=1:4, pch=1:4, xlab='logit', main=' ', xlim=c(-5,5), cex.lab=0.7, cex.axis=0.5, cex.sub=0.5)

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel1 <- predict(eq_OLR_01, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_01, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel1)

##     predictedLevel1
##      R1 R2 R3 R4
##   R1  3 15  1  0
##   R2  3 82  8  0
##   R3  0 17 56 11
##   R4  0  0 13 15

p1 <- mean(as.character(test.data$risk) != as.character(predictedLevel1)) #misclassification error
p1

## [1] 0.3035714

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel2 <- predict(eq_OLR_02, test.data) # predict the levels directly

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel2)

##     predictedLevel2
##      R1 R2 R3 R4
##   R1  3 15  1  0
##   R2  2 83  8  0
##   R3  0 16 56 12
##   R4  0  0 12 16

p2 <- mean(as.character(test.data$risk) != as.character(predictedLevel2))
p2

## [1] 0.2946429

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel3 <- predict(eq_OLR_03, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_03, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel3)

##     predictedLevel3
##      R1 R2 R3 R4
##   R1  3 15  1  0
##   R2  2 83  8  0
##   R3  0 18 57  9
##   R4  0  0 10 18

p3 <- mean(as.character(test.data$risk) != as.character(predictedLevel3))
p3

## [1] 0.28125

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel4 <- predict(eq_OLR_04, test.data) # predict the levels directly

predictedScores1 <- predict(eq_OLR_04, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel4)

##     predictedLevel4
##      R1 R2 R3 R4
##   R1  3 15  1  0
##   R2  2 83  8  0
##   R3  0 18 57  9
##   R4  0  0 10 18

p4 <- mean(as.character(test.data$risk) != as.character(predictedLevel4))
p4

## [1] 0.28125

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel5 <- predict(eq_OLR_05, test.data) # predict the levels directly

predictedScores5 <- predict(eq_OLR_05, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel5)

##     predictedLevel5
##      R1 R2 R3 R4
##   R1  3 15  1  0
##   R2  3 82  8  0
##   R3  0 18 57  9
##   R4  0  0 13 15

p5 <- mean(as.character(test.data$risk) != as.character(predictedLevel5))
p5

## [1] 0.2991071

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

predictedLevel6 <- predict(eq_OLR_06, test.data) # predict the levels directly

predictedScores6 <- predict(eq_OLR_06, test.data, type="p") 
 # predict the probabilites

## Confusion matrix and misclassification error
table(test.data$risk, predictedLevel6)

##     predictedLevel6
##      R1 R2 R3 R4
##   R1  2 16  1  0
##   R2  2 83  8  0
##   R3  0 20 56  8
##   R4  0  0 12 16

p6 <- mean(as.character(test.data$risk) != as.character(predictedLevel6))
p6

## [1] 0.2991071

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

#Table 

df2 <- data.frame(
  
  "Equations"=c(1:6), 
  "Predicted"=c(1-p1, 
                1-p2,
                1-p3,
                1-p4,
                1-p5,
                1-p6
               
              
    
    
  )
  
  
  
)

df2

##   Equations Predicted
## 1         1 0.6964286
## 2         2 0.7053571
## 3         3 0.7187500
## 4         4 0.7187500
## 5         5 0.7008929
## 6         6 0.7008929

Diagnostic - one model

Erica Goto

2/10/2019

Library & Dataset

Inspect Dataset Using Training and Validation

OLR Equations

Inspect Dataset Using Training and Validation

Equation 1

Creating function with four level

Equation 2

Equation 3

Equation 4

Equation 5 - Based on Equation 1

OLR Equation 6

Predicion on test data Eq 1: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 2: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 3: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 4: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 5: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 6: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html

Predicion on test data Eq 7: http://r-statistics.co/Ordinal-Logistic-Regression-With-R.html